3D point clouds are used in various image processing and computer vision applications. 3D point clouds are sets of data points in a 3D coordinate system typically representing an external surface of an object. 3D point clouds may be obtained by a 3D capturing device, such as a 3D scanner. A large number of points are measured on the surface of an object, and the obtained point cloud may be stored in a file.
Various sensing methods for obtaining 3D point clouds have been developed. For example, in Structure-From-Motion (SFM), three-dimensional structures are estimated from two-dimensional image sequences, where the observer and/or the objects to be observed move in relation to each other. In Light Detection And Ranging (LiDAR) method, distances are measured by illuminating an object with a laser beam and analyzing the reflected light. The resulting data is stored as point clouds.
In applications of multiple views, combining several point clouds into a global consistent data set is typically required. The problem of matching a given set of 3D point clouds with another is a challenging task in computer vision. This problem becomes even more challenging when two sets of points are yielded by different sensing techniques.
Typically, point clouds obtained by different sensing techniques may be incompatible at least in terms of scale, point density, noise and/or sampling area.